Sika Deer Facial Recognition Model Based on SE-ResNet

Enhancing deterministic prediction in unidirectional ocean waves using an Artificial Neural Network with exponential linear unit

Focusing on the problems of complex structure and low feature extraction efficiency that exist in some traditional neural network algorithms, an improved convolutional neural network, combined with a convolutional attention mechanism, is proposed to im- prove the network feature extraction efficiency and reduce the network complexity. First, the convolutional block attention module (CBAM) was introduced, which can strengthen the extraction of useful features from the data, thereby improving the network feature extraction efficiency. Second, the residual structure was optimized, and the original multi-layer convolution was simplified to a single-layer convolution, which reduces the complexity of the model and improves the diagnostic efficiency. Then, the Rectified Linear Unit (ReLU) activation function in the model was replaced with the better-performing Exponential Linear Unit (ELU) activation function. Finally, the overall structure of the ResNet model was optimized. The CBAM-ResNet network model was based on the traditional ResNet18 model and consists of a CBAM module and the optimized ResNet18 model. Finally, experiments were performed using sensor data collected from rolling bearing test benches. The experimental results show that the feature extraction capability and efficiency of the new model are better than those of several other classic deep network models and the original network model, and this reduces the time complexity and model size while also maintaining a high accuracy rate.

Price movement predictions of financial instruments using traditional time-series models with a predefined mathematical structure are common, but this method restricts their ability to learn latent patterns in the data. In recent times, artificial neural networks (ANN) have been able to learn complex hidden patterns from financial datasets using a highly nonlinear architecture. However, most experiments with neural networks require a lot of time to search a suitable network and subsequently train the network. The authors have developed a deep multilayer perceptron (MLP) classifier with a zero-centered piecewise linear unit activation that yielded better classification performance according to their accuracy metric and required consistently less training time compared to a similar MLP network with leaky rectified linear unit and tanh activation functions. The authors illustrate their technique with a large high-frequency dataset on selected bank shares from the Indian stock market. The authors also discuss the theoretical properties and advantages of their proposal. <b>TOPICS:</b>Statistical methods, simulations, big data/machine learning <b>Key Findings</b> • Time-series models have been used extensively to understand stock price movements. They are constrained by a predefined structure. Artificial neural network (ANNs), due to their universal approximation properties, offer more flexibility; however, they suffer from long training times. • In this article, the authors introduce a zero-centered piecewise linear unit (PLU), a hybrid activation function derived from tanh and leaky rectified linear unit (ReLU) functions that exhibits the zero-centered property from tanh and the nonzero gradient property from leaky ReLU. • The authors developed a multilayer perceptron (MLP) classifier with PLU activation, which yielded better classification performance according to the accuracy metric and required consistently less training time compared to a similar MLP network with leaky ReLU and tanh activation.

Recently, Deep learning has made a great deal of success in processing images, audios, and natural languages and so on. The activation function is one of the key factors in Deep learning. In this paper, according to characteristics of biological neurons, an improved Leaky Single-Peaked Triangle Linear Unit (LSPTLU) activation function is presented for the right-hand response unbounded of Rectified Linear Unit (ReLU) and Leaky ReLU (LReLU). LSPTLU is more in line with the biological neuron essence and achieves the excellent performance of equivalent or beyond ReLU and LReLU on different datsets, e.g., MNIST, Fashion-MNIST, SVHN, IMAGENET, CALTECH101 and CIFAR10 datasets.

Different parameters of a fully convolutional network (FCN) are experimented to evaluate which combination predicts sound velocity models from a single configuration of seismic modeling. The evaluation is made considering some fixed parameters of the deep learning model, such as number of epochs, batch size and loss function, but with variations of the optimizer and activation function. The considered optimizers were RMSprop, Adam and Adamax, whilst the activations functions were the Rectified Linear Unit (ReLU), Leaky ReLU, Exponential Linear Unit (ELU) and Parametric ReLU (PReLU). Five metrics were used to evaluate the model during the testing stage: R2, Pearson's r, factor of two, mean absolute error and mean squared error. To the extent of these experiments, it was found that the optimizers have much more influence than the activation functions when determining the resolution of the output model. The best combination was the one using the PReLU activation function with the Adamax optimizer.

Recognizing human activity is a challenging task in many applications like visual surveillance, Human–computer interaction, etc. Numerous machine learning (ML) and deep learning (DL) approaches are used to emulate the human behavior of everyday life, as they provide better recognition by learning complex features. This paper proposes an innovative human activity recognition framework with the following steps. Initially, the given video is converted into a set of frames and then applies median filtering to remove the noise for efficient recognition. Subsequently, the pixel-relatedness retrieval-fuzzy clustering means algorithm (PRR-FCM) approach is proposed for image segmentation, in which a pixel-relatedness retrieval strategy is used that considers only the proximity-related pixel for efficient segmentation. The retrieval of features is an important aspect, thereby this paper goes with the extraction of texture features, relative standard deviation induced in multi-texon (RSIMT), in which relative standard deviation is induced for interchanging the center pixel value. Also, the other features like the hierarchy of the skeleton and local binary pattern (LBP) are extracted from the segmented image. Further, the retrieved features are fed to the hybrid model that combines models like modified deep Maxout (MDM) and convolution neural network (CNN) models to recognize human activity efficiently. The modified exponential linear unit (ELU) activation function and Log loss function are the enhancing formulation of the MDM model to determine accurate recognition results.

The development of a Social Intelligence System based on artificial intelligence is one of the cutting edge technologies in Assistive Robotics. Such systems need to create an empathic interaction with the users; therefore, it os required to include an Emotion Recognition (ER) framework which has to run, in near real-time, together with several other intelligent services. Most of the low-cost commercial robots, however, although more accessible by users and healthcare facilities, have to balance costs and effectiveness, resulting in under-performing hardware in terms of memory and processing unit. This aspect makes the design of the systems challenging, requiring a trade-off between the accuracy and the complexity of the adopted models. This paper proposes a compact and robust service for Assistive Robotics, called Lightweight EMotion recognitiON (LEMON), which uses image processing, Computer Vision and Deep Learning (DL) algorithms to recognize facial expressions. Specifically, the proposed DL model is based on Residual Convolutional Neural Networks with the combination of Dilated and Standard Convolution Layers. The first remarkable result is the few numbers (i.e., 1.6 Million) of parameters characterizing our model. In addition, Dilated Convolutions expand receptive fields exponentially with preserving resolution, less computation and memory cost to recognize the distinction among facial expressions by capturing the displacement of the pixels. Finally, to reduce the dying ReLU problem and improve the stability of the model, we apply an Exponential Linear Unit (ELU) activation function in the initial layers of the model. We have performed training and evaluation (via one- and five-fold cross validation) of the model with five datasets available in the community and one mixed dataset created by taking samples from all of them. With respect to the other approaches, our model achieves comparable results with a significant reduction in terms of the number of parameters.

Emotion recognition still remains a research issue that can be improved significantly. This paper worked on it using facial feature extraction followed by arousal level estimation. We extracted facial features via four Convolutional Neural Networks (CNNs) pre-trained on ImageNet: ResNet152V2, Xception, ResNeXt101, and InceptionResNetV2 based on low memory usage and modernity. For estimating arousal levels, Exponential Linear Unit (ELU) was compared with Rectified Linear Unit (ReLU) in a custom deep model framework. Wild facial expression image data was taken from the relatively new AffectNet database. Up to this point, recognition of emotions from AffectNet is largely treated as a classification problem, with arousal level estimation mostly unexplored. Our results showed that the custom deep model trained on multiple classes and fine-tuned on one class was more effective than that trained and tested on the same class. ResNet152V2 displayed the best performance among the CNNs, emphasizing its suitability for the CultureNet-based architecture from which this algorithm is based. Further research includes more activation functions, ResNet variants, and the application of a softmax layer.

Classical Convolutional Neural Networks, or ConvNets, have been the benchmarks for most object classification and face recognition tasks despite suffering from major limitations such as the inability to capture spatial co-locality between data points and favoring invariance over equivariance. Hence, a hierarchical routing layered architecture called Capsule Networks was proposed to overcome shortcomings of ConvNets. Capsules replace average or max pooling techniques of ConvNets with dynamic routing abilities between lower level and higher level neural units which better capture hierarchical relationships within the data and introduced reconstruction regularization mechanisms which deals with equivariance properties. By overcoming existing limitations, Capsules have proven themselves to be potential benchmarks in object segmentation, detection and reconstruction. Capsules have achieved state of the art results on the fundamental MNIST (Modified National Institute of Standards and Technology) handwritten digit dataset by reducing the ConvNets test error benchmark of 0.39% to 0.25%. In order to further augment this distinction, we experimented with five activation units such as sigmoid, e-Swish, Swish, variants of Rectified Linear Units (ReLU) like Parametric ReLU (PReLU), leaky ReLU (lReLU) and Scaled Exponential Linear Units (SELU), on two fundamental datasets - MNIST and cifar10. Based on these experimental results, we establish that e-Swish, and ReLU variants better optimize the Capsule architecture as compared to the currently used ReLU activation function.

The CSP process is compact, and pseudo defects such as water strain, seam, and rolling scales cause the surface of slab to be very complicated, so it is necessary to develop a fast and accurate defect recognition model. In this paper, an improved ResNet is proposed to identity the surface defects of typical CSP slabs. The original ResNet model struc-ture is adjusted to halve the channel of convolution kernels, which can reduce the amount of calculation and enable the network to run under low conditions. Meanwhile, the location of the activation function is changed so that it is not used for shortcut connections, and we use exponential linear units (ELU) instead of rectified linear units (ReLU), which can speed up convergence and enhance model generalization ability. The results show that the defect detection accuracy of improved ResNet is 84%, which is 4% higher than that of original network, and the pseudo defect detection accuracy of improved ResNet is 97%, which is 1% higher than that of original network. The proposed network parameters are only 6 million, and the test time is 31 milliseconds.

A convolutional neural network is employed to retrieve the time-domain envelop and phase of few-cycle femtosecond pulses from transient-grating frequency-resolved optical gating (TG-FROG) traces. We use theoretically generated TG-FROG traces to complete supervised trainings of the convolutional neural networks, then use similarly generated traces not included in the training dataset to test how well the networks are trained. Accurate retrieval of such traces by the neural network is realized. In our case, we find that networks with exponential linear unit (ELU) activation function perform better than those with leaky rectified linear unit (LRELU) and scaled exponential linear unit (SELU). Finally, the issues that need to be addressed for the retrieval of experimental data by this method are discussed.

In modern power system, along with the developments of the data collecting technologies, the intensive and high-dimensional load data collection can be achieved. Therefore, to deeply reveal the patterns and behaviors hidden in the load dataset using load classification is of great significance for improving the service quality and the user experience of the power system. However, inevitable issues, for example the data missing and class imbalance are frequently reported in the present load dataset, which deteriorates the performance of the classification algorithms. Also, due to the special features, for example the time series, periodicity, and fluctuation of the load data, the traditional data classification algorithms also encounter performance defects. Therefore, this paper presents a data augmentation based enhanced temporal convolutional network (TCN) algorithm in enabling load classification. In the data augmentation phase, first an LRTC-TSVD algorithm is presented to implement the missing data completion. Second, a WGAN based class balancing approach is further presented to solve the class imbalance issue. Then, in the enhanced TCN phase, a WeightNorm, exponential linear unit (ELU) activation function, residual connection, and bidirectional feature fusion techniques based improved TCN (ITCN) algorithm is presented to carry out the accurate load data classification. Combining the data augmentation and the enhanced TCN phases, the ITCN algorithm is finally conducted. Based on the benchmark load datasets, the performances of the presented ITCN are evaluated. The experimental results report that the presented data augmentations can improve the quality of the dataset, moreover the classification algorithm is able to achieve the satisfied classification accuracy.

For the internet of medical things (IoMT) enabled long-term health monitoring and disease prediction applications, there is a demand for an automatic photoplethysmogram (PPG) signal quality assessment (SQA) for reducing false alarms and energy consumption. This paper presents a PPG-SQA method by using raw PPG signal and convolutional neural network (CNN) with optimal parameters. The main focus of this paper is to find an optimal number of filters (16, 32, 64) and number of layers (2 and 4 layers) with rectified linear unit (ReLU) activation function and to study robustness of trained CNN models by using unseen PPG datasets and different kinds of noise sources, which are not addressed in the past studies on the CNN-based PPG-SQA methods. Evaluation results showed that the 4 layer CNN-based method had the higher accuracy of 99.58% for noise-free PPG (NF-PPG) versus wrist-cup noisy PPG signal database (MA-DB01), 99.99% for NF versus random noises added PPG (RN-PPG) signals, and 75.80% for NF-PPG versus acceleration corrupted PPG signals (MA-DB02). For the unknown dataset, the 4-layer CNN model had the higher accuracy of 96.71% for NF-PPG versus MA-DB01 and 99.04% for NF versus RN-PPG, and the 2-layer CNN model had the higher accuracy of 76.16% for NF-PPG versus MA-DB02 PPG segments. Results demonstrate that the CNN-based PPG-SQA method with optimal parameters is not only improve the accuracy but can also reduce the computational load as compared with other existing methods.

This paper presents a novel multimodal medical image fusion framework (M3IF-NSST-CNN) combining the Non-Subsampled Shearlet Transform (NSST) and deep convolutional neural network (CNN) methods to enhance diagnostic accuracy by effectively integrating Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Initially, both CT and MRI images undergo decomposition via NSST, producing distinct low-pass (LPSB) and high-pass subbands (HPSB). The LPSB fusion leverages a correlation-based approach, ensuring preservation of structural integrity and maximization of complementary information. Concurrently, the HPSB are merged through a Universal Image Quality Index (UIQI)-based fusion rule, prioritizing edge and detail retention critical for precise medical diagnosis. Subsequently, an inverse NSST transforms these fused sub-bands, generating a preliminary fused image. This image is then further refined through a CNN-based noise reduction module comprising convolutional layers, batch normalization, and Rectified Linear Unit (ReLU) activation functions. The deep CNN architecture progressively refines the fused output, significantly reducing artifacts and noise, thus improving the overall clarity and interpretability of the resultant image. This approach addresses common limitations in image fusion, such as loss of edge details, distortion, and the inability to preserve critical information. The hybrid M3IF-NSST-CNN method improves both the visual quality and parametric accuracy of the fusion, outperforming comparable techniques. By preserving more detail and edge information, this method offers significant advancements in multi-modal medical image fusion for enhanced diagnostic insights.

Hybrid surrogate model for online temperature and pressure predictions in data centers